--- title: "RAG-FiT vs filco" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/intellabs-rag-fit-vs-zorazrw-filco" tools: ["intellabs-rag-fit", "zorazrw-filco"] --- # RAG-FiT vs filco *GraphCanon updated Sep 20, 2026* ## Verdict Pick RAG-FiT if rAG-FiT is a Python framework that enables developers to fine-tune large language models specifically for Retriever-Augmented Generation (RAG) tasks, with strengths in evaluation and information retrieval; pick filco if filco is a Python-based project that focuses on dialog generation, fact verification, and question answering using models like Flan-T5 and LLaMA2, with Dense Passage Retriever for retrieving relevant. [RAG-FiT](https://intellabs.github.io/RAG-FiT/) reports 768 GitHub stars, 62 forks, and 1 open issues, last pushed Jun 8, 2026. [filco](https://arxiv.org/pdf/2311.08377.pdf) has 199 stars, 20 forks, and 2 open issues, last pushed Apr 6, 2024. Figures are from public GitHub metadata via [RAG-FiT's repository](https://github.com/IntelLabs/RAG-FiT) and [filco's repository](https://github.com/zorazrw/filco). | | [RAG-FiT](/tools/intellabs-rag-fit.md) | [filco](/tools/zorazrw-filco.md) | | --- | --- | --- | | Tagline | Framework for enhancing LLMs for RAG tasks using fine-tuning | Learning to Filter Context for Retrieval-Augmented Generation | | Stars | 768 | 199 | | Forks | 62 | 20 | | Open issues | 1 | 2 | | Language | Python | Python | | Adopt for | RAG-FiT is a Python framework that enables developers to fine-tune large language models specifically for Retriever-Augmented Generation (RAG) tasks, with strengths in evaluation and information retrieval. | Filco is a Python-based project that focuses on dialog generation, fact verification, and question answering using models like Flan-T5 and LLaMA2, with Dense Passage Retriever for retrieving relevant Wikipedia passages. | | Persona | - | - | | Runtime | - | - | | License | RAG-FiT operates under the Apache-2.0 license, providing a permissive free software license that permits reuse within proprietary software. | CC-BY-SA-4.0 | | Categories | Evaluation & Observability, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RAG-FiT](/tools/intellabs-rag-fit.md) | [filco](/tools/zorazrw-filco.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 102d | 894d | | Open issues (now) | 1 | 2 | | Stars delta | 0 (30d) | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/intellabs-rag-fit/trust.md) | [trust report](/tools/zorazrw-filco/trust.md) | ## Shared compatibility - **Python**: [RAG-FiT](/tools/intellabs-rag-fit.md) - Python runtime; [filco](/tools/zorazrw-filco.md) - Python runtime ## Decision facts: RAG-FiT - **Requirements:** This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized. - **Adopt for:** RAG-FiT is a Python framework that enables developers to fine-tune large language models specifically for Retriever-Augmented Generation (RAG) tasks, with strengths in evaluation and information retrieval. - **License detail:** RAG-FiT operates under the Apache-2.0 license, providing a permissive free software license that permits reuse within proprietary software. ## Decision facts: filco - **Adopt for:** Filco is a Python-based project that focuses on dialog generation, fact verification, and question answering using models like Flan-T5 and LLaMA2, with Dense Passage Retriever for retrieving relevant Wikipedia passages. ## Choose when ### Choose RAG-FiT if… - License: RAG-FiT is Apache-2.0, filco is CC-BY-SA-4.0. - Requirements: This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized.. - Tags unique to RAG-FiT: evaluation, fine-tuning, information retrieval, llm. - Also covers Evaluation & Observability. - When seeking to improve performance of LLMs in NLP tasks requiring RAG capabilities, like question-answering or semantic search ### Choose filco if… - License: filco is CC-BY-SA-4.0, RAG-FiT is Apache-2.0. - Tags unique to filco: dialog-generation, fact-verification, flan-t5, llama2. - Also covers Data & Retrieval. - When you need to generate dialogues that require fact verification and question answering capabilities, leveraging models like Flan-T5 and LLaMA2. ## When NOT to use RAG-FiT - If project needs are more aligned with traditional fine-tuning methods that do not specifically enhance RAG capabilities, another tool might be more suitable - In scenarios where the development team lacks proficiency in Python, as RAG-FiT is Python-based and may have a steeper learning curve for non-Python developers ## When NOT to use filco - If your project does not require the use of Flan-T5 or LLaMA2 models for dialog generation, fact verification, or question answering. - When you do not need to retrieve relevant Wikipedia passages using Dense Passage Retriever. - If your project does not involve the use of preprocessed datasets with top-5 retrieved passages from Wikipedia. ## Common questions ### What is the difference between RAG-FiT and filco? RAG-FiT: Framework for enhancing LLMs for RAG tasks using fine-tuning. filco: Learning to Filter Context for Retrieval-Augmented Generation. See the comparison table for live GitHub stats and shared categories. ### When should I choose RAG-FiT over filco? Choose RAG-FiT over filco when License: RAG-FiT is Apache-2.0, filco is CC-BY-SA-4.0; Requirements: This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized.; Tags unique to RAG-FiT: evaluation, fine-tuning, information retrieval, llm; Also covers Evaluation & Observability; When seeking to improve performance of LLMs in NLP tasks requiring RAG capabilities, like question-answering or semantic search. ### When should I choose filco over RAG-FiT? Choose filco over RAG-FiT when License: filco is CC-BY-SA-4.0, RAG-FiT is Apache-2.0; Tags unique to filco: dialog-generation, fact-verification, flan-t5, llama2; Also covers Data & Retrieval; When you need to generate dialogues that require fact verification and question answering capabilities, leveraging models like Flan-T5 and LLaMA2. ### When should I avoid RAG-FiT? If project needs are more aligned with traditional fine-tuning methods that do not specifically enhance RAG capabilities, another tool might be more suitable In scenarios where the development team lacks proficiency in Python, as RAG-FiT is Python-based and may have a steeper learning curve for non-Python developers ### When should I avoid filco? If your project does not require the use of Flan-T5 or LLaMA2 models for dialog generation, fact verification, or question answering. When you do not need to retrieve relevant Wikipedia passages using Dense Passage Retriever. If your project does not involve the use of preprocessed datasets with top-5 retrieved passages from Wikipedia. ### Is RAG-FiT or filco more popular on GitHub? RAG-FiT has more GitHub stars (768 vs 199). Stars measure visibility, not whether either tool fits your constraints. ### Are RAG-FiT and filco open source? Yes - both are open-source projects on GitHub (RAG-FiT: Apache-2.0, filco: CC-BY-SA-4.0). ### Where can I find alternatives to RAG-FiT or filco? GraphCanon lists graph-backed alternatives at [RAG-FiT alternatives](/tools/intellabs-rag-fit/alternatives) and [filco alternatives](/tools/zorazrw-filco/alternatives) ([RAG-FiT markdown twin](/tools/intellabs-rag-fit/alternatives.md), [filco markdown twin](/tools/zorazrw-filco/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/intellabs-rag-fit-vs-zorazrw-filco.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, RAG-FiT or filco? RAG-FiT: Slowing. filco: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for RAG-FiT and filco? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RAG-FiT trust report](/tools/intellabs-rag-fit/trust); [filco trust report](/tools/zorazrw-filco/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=intellabs-rag-fit`](/api/graphcanon/graph?tool=intellabs-rag-fit) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_